Can a stronger model lift a weaker one at test time without retraining?

The paper asks whether strong-to-weak transfer, usually done by distillation that updates the smaller model's parameters, can instead happen at test time. A stronger builder model constructs an inference-time harness for a weaker target model and refines it over several rounds against a validation set that is 5% of the data, across four Theory-of-Mind benchmarks. The abstract reports average target-model performance "nearly doubling" from 0.49 to 0.91. The discussion gives the finer picture over 72 runs: a mean uplift of +0.275 over the GPT-5.4-mini no-scaffold baseline, every run and all 11 builder configurations above baseline, and a best scaffold at 0.912 (+0.423). The 0.91 headline therefore appears to be the best scaffold, not the mean. That best scaffold approaches a human-inspired harness reference (0.939) on the same backbone "despite using no human ToM-specific engineering."

The paper's framing is that a small model's failure "may reflect not only insufficient internal capability, but also excessive cognitive load imposed by the way the task is presented." Its analysis locates the gains "primarily" in three moves: offloading unstable model reasoning into deterministic code, benchmark-specific routing, and strict answer-format enforcement. It says they do not come from encouraging the target model to reason more extensively or sample more broadly. The procedure is reported as reproducible but not deterministic: mean within-cell standard deviation is 0.036, roughly an order of magnitude below the uplift, and the remaining variance sits in deterministic-solver strategies where one implementation error can swing a benchmark.

This sits awkwardly beside Can non-reasoning models catch up with more compute?, which finds that more inference compute does not close a training-regime gap. The two agree that extra reasoning or sampling is not the lever. They differ in what fills the gap: here a harness written by a stronger model carries much of the load outside the weights. One reading is that a harness supplies structure rather than manufacturing capability, but the excerpt does not test that. In the How do knowledge injection methods trade off flexibility and cost?, a harness is closest to prompt optimization, since nothing is trained. Yet deterministic code and routing are not prompt content, so it fits that category only loosely. It also echoes Does RL post-training create reasoning or just deploy it? in placing part of the gap in deployment rather than raw capability, though by relocating work out of the model, not by eliciting it.

The excerpt does not establish how far this generalizes. Results cover four Theory-of-Mind benchmarks, and the analysis credits "benchmark-specific routing" as one source of gain, so the excerpt gives no evidence for tasks outside them. It does not show that the target model reasons better, and the stated mechanism suggests it mostly does not. The "validation-efficient" claim is cut off before any detail. At the strength supported, a harnessed weak model's benchmark score reflects the harness as much as the model, and part of the measured weak-to-strong gap on these tasks is a presentation and execution gap that can be closed without touching parameters.